Depressive symptoms and loneliness among early adolescents: a psychometric network analysis approach

Associations among screen time, depressive symptoms and sleep in early adolescents: A sex-disaggregated cross-lagged network analysis

BACKGROUND

Prior research has established associations among screen time, depressive symptoms, and sleep. However, the nuances of these associations, particularly regarding directionality and sex-specific differences among early adolescents, remain insufficiently understood. This study aimed to investigate sex differences in the longitudinal associations among screen time, depressive symptoms, and sleep at the symptom level during early adolescence.

METHODS

Our cohort consisted of 2987 students (1451 boys at T1; Mage = 10.52 years), who were assessed twice over six-month intervals. Sex-stratified analyses in the network associations among screen time, depressive symptoms, and sleep were examined via a cross-lagged panel network modeling approach.

RESULTS

The results revealed that depressive symptoms such as "feeling unloved" for boys and "sadness" for girls were the most central symptoms with the greatest influence on other symptoms. Furthermore, depressive symptoms such as "crying" and "loneliness" for boys and "self-hatred" and "loneliness" for girls may act as bridge symptoms, significantly forecasting excessive screen time after six months.

CONCLUSIONS

Our findings bolster the compensatory internet use theory by suggesting that screen time may function as a coping mechanism for managing depressive symptoms and sleep problems. These findings advance our comprehension of the evolving dynamics among screen time, depressive symptoms, and sleep across sexes over time. The development of targeted prevention and intervention strategies that accommodate these sex differences could be instrumental in curtailing the onset or intensification of problematic screen use among early adolescents.

Mapping vulnerability factors of chronic emotional and social loneliness: A network analysis

BACKGROUND

Chronic loneliness adversely impacts both mental and physical health. As current interventions are only moderately effective, examining vulnerability factors reflected in interpersonal behavior and their relationships with specific chronic loneliness subtypes may provide insights. The present study explored the associations between loneliness vulnerability factors and two subtypes of chronic loneliness: chronic emotional loneliness (a longing for intimate connections) and chronic social loneliness (dissatisfaction with the quantity and quality of one's social network).

METHODS

The sample consisted of 294 university students who had recently started at a new university. Participants completed surveys across three time points, separated by three-month intervals, including questionnaires on emotional and social loneliness, adult attachment styles, interpersonal behavioral styles, and features of emotional disorders. To explore relationships, we used network analysis by estimating a Mixed Graphical Model (MGM), in which chronic emotional loneliness and chronic social loneliness were dichotomized as binary variables (absence versus presence), while vulnerability factors were treated as continuous variables.

RESULTS

The network analysis demonstrated that chronic loneliness subtypes were differentially linked to vulnerability factors. Chronic emotional loneliness was predominantly linked to features of emotional disorders, whereas chronic social loneliness demonstrated a negative conditional association with secure attachment style.

LIMITATIONS

The assessment of chronic loneliness was based on three time points, which does not capture potential fluctuations between assessments.

CONCLUSIONS

These findings highlight the importance of differentiating between chronic loneliness subtypes when examining vulnerability factors. By distinguishing these subtypes, tailored interventions for loneliness can be developed to enhance prevention.

Exploring the interconnections of loneliness, anxiety, and depression among nursing students: a network analysis approach

Objective

Nursing students experience higher rates of anxiety and depression than students in other disciplines due to the demanding academic requirements and clinical training challenges. Loneliness and symptoms of anxiety and depression occur simultaneously; however, the specific interrelationships between these states remain inadequately investigated. This study aimed to utilize network analysis to examine the item-level reciprocal action between loneliness, anxiety, and depression among nursing students.

Methods

A total of 888 nursing students were assessed using the short-form UCLA Loneliness Scale (ULS-6), the Generalized Anxiety Disorder 7-item Questionnaire (GAD-7), and the Patient Health Questionnaire-9 (PHQ-9) on loneliness, anxiety, and depression, respectively. Descriptive analyses were conducted using SPSS 26.0, whereas other statistical analytical procedures were performed using R software. The Gaussian graphical model was used to estimate network, and the Network Comparison Test was applied to compare differences in networks across gender and grades.

Results

The results indicated that 58.6% of nursing students exhibited varying degrees of loneliness. Network analysis revealed that loneliness formed a separate cluster with limited connections to anxiety and depression communities. The edges between PHQ3-PHQ4 (insomnia or hypersomnia and fatigue), GAD1-GAD2 (feeling anxious and excessive worrying), ULS1-ULS2 (lonely and no one) showed the strongest positive edges within their communities, respectively. And the strongest inter-community edges were observed between GAD5-PHQ8 (fidgety-retardation), ULS6-PHQ4 (isolation-fatigue), and ULS1-GAD1 (lonely-feeling anxious). The centrality analysis identified GAD2 (excessive worrying), ULS6 (isolation), PHQ4 (fatigue), and PHQ2 (feeling down) as the most central node, indicating their significant influence on the overall network structure. Additionally, PHQ8 (retardation), PHQ2 (feeling down), GAD5 (fidgety), and GAD1(feeling anxious) played a crucial role as bridging symptoms that linked the three communities. In addition, there is no statistically significant difference in the network structure except strength of GAD3 (generalized anxiety) and GAD6 (irritable) between sexes.

Conclusions

This study highlights the high prevalence of loneliness among nursing students and its distinct yet limited connection to anxiety and depression, emphasizing its unique role as a standalone psychological construct. The central symptoms in the network and important bridge symptoms across different psychological communities highlight the complexity of mental health symptom networks. This underscores the importance of targeting central symptoms for domain-specific interventions and addressing bridge symptoms to mitigate comorbidities across psychological conditions among nursing students.

Neural correlates of anhedonia in young adults with subthreshold depression: A graph theory approach for cortical-subcortical structural covariance

BACKGROUND

Anhedonia is an enduring symptom of subthreshold depression (StD) and predict later onset of major depressive disorder (MDD). Brain structural covariance describes the inter-regional distribution of morphological changes compared to healthy controls (HC) and reflects brain maturation and disease progression. We investigated neural correlates of anhedonia from the structural covariance.

METHODS

T1-weighted brain magnetic resonance images were acquired from 79 young adults (26 StD, 30 MDD, and 23 HC). Intra-individual structural covariance networks of 68 cortical surface area (CSAs), 68 cortical thicknesses (CTs), and 14 subcortical volumes were constructed. Group-level hubs and principal edges were defined using the global and regional graph metrics, compared between groups, and examined for the association with anhedonia severity.

RESULTS

Global network metrics were comparable among the StD, MDD, and HC. StD exhibited lower centralities of left pallidal volume than HC. StD showed higher centralities than HC in the CSAs of right rostral anterior cingulate cortex (ACC) and pars triangularis, and in the CT of left pars orbitalis. Less anhedonia was associated with higher centralities of left pallidum and right amygdala, higher edge betweenness centralities in the structural covariance (EBSC) of left postcentral gyrus-parahippocampal gyrus and LIPL-right amygdala. More anhedonia was associated with higher centralities of left inferior parietal lobule (LIPL), left postcentral gyrus, left caudal ACC, and higher EBSC of LIPL-left postcentral gyrus, LIPL-right lateral occipital gyrus, and left caudal ACC-parahippocampal gyrus.

LIMITATIONS

This study has a cross-sectional design.

CONCLUSIONS

Structural covariance of brain morphologies within the salience and limbic networks, and among the salience-limbic-default mode-somatomotor-visual networks, are possible neural correlates of anhedonia in depression.

Prevalence and Risk by Age and Sex of Sleep Dysregulation and Depressive Episodes in Bipolar and Depressive Disorders in a Community Survey in Sardinia, Italy

Background/Objectives: Sleep disturbances often accompany mood disorders and persistent insomnia after mood symptoms have resolved may be a marker of poor outcome. The association between sleep symptoms and mood disorders seems to change with age and sex. This study aims to assess the frequency of depressive episodes and sleep disorders in the general population through an agile screening questionnaire and to evaluate the association of depressive episodes and sleep symptoms by sex and age categories. Methods: 774 women and 728 men from Sardinia aged > 16 years old were enrolled. The Patient Health Questionnaire (PHQ-9) was administered through a computer-assisted telephonic interview. Results: The frequency of depressive episodes was double in women (10.6% vs. 4.4%; p < 0.0001), with the highest values in women > 75 yo (17.4%). The frequency of sleep dysregulation was double in women (18.7% vs. 9.6%; p < 0.0001), with the highest values in women > 75 yo (35.9%) and the lowest in the group of men > 75 yo. The group of young males showed the lowest frequency of depressive episodes (1.4%) and a frequency of sleep dysregulation (9.1%) similar to that of the other groups of age and sex. Sleep dysregulation without depressive episodes presented a higher distribution in the elderly, both in males (20.7%) and in females (18.5%). No significative differences were found across sex and age groups in the distribution of depressive episodes without sleep dysregulation. Conclusions: The use of an agile screener such as PHQ9 in the general population and/or in populations at risk can be a valuable tool in finding those individuals in whom sleep dysregulation may represent an early warning signal, one that may be thoroughly evaluated to identify and treat possible sleep disorders early.

Loneliness and brain rhythmic activity in resting state: an exploratory report

Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.

Growth mindset and well-being in social interactions: countering individual loneliness

Introduction

Loneliness is a prevalent negative emotion experienced by college students. This study explores the relationship between a growth mindset and loneliness among college students.

Methods

A total of 560 college students completed the Growth Mindset Scale (GMS), UCLA Loneliness Scale (UCLA), Interpersonal Relationships Assessment Scale (IRS), and two measures assessing distinct facets of well-being the Satisfaction with Life Scale (SWLS) and the revised Positive Affect and Negative Affect Scale (PANAS).

Results and discussion

The results found a significant negative correlation between a growth mindset and loneliness. A growth mindset negatively predicted loneliness through the chain-mediated effects of interpersonal distress and well-being. These findings underscore the important role of a growth mindset in influencing loneliness, providing teachers and practitioners a new perspective to understand and intervene college students' psychological challenges.

How loneliness linked to anxiety and depression: a network analysis based on Chinese university students

BACKGROUND

There is conclusive evidence of a multifaceted and bidirectional relationship between loneliness and depression and anxiety. Nonetheless, more extensive research is needed to examine their relationships at a more granular level. This study employed a network analysis approach to identify the pathological mechanisms underpinning those relationships and to identify important bridge nodes as potential targets for intervention.

METHODS

941 University students were included in this study. The ULS-6 (the short-form UCLA Loneliness Scale) was used to assess loneliness, the PHQ-9 (Patient Health questionnaire-9) and GAD-7 (Generalized anxiety disorder 7-item) scales were used to assess the symptoms of depression and anxiety. We constructed two network structures of loneliness-anxiety and loneliness-depression and computed bridge expected influence for each symptom. In addition, we showed a flow network of "Suicide" containing symptoms of depression and loneliness.

RESULTS

All edges were positive in both networks constructed and the strongest edges were present within disorder communities. The overall connection between loneliness and depression was stronger compared to anxiety. The results demonstrated that the loneliness item "People are around me but not with me" was identified as bridge symptom in both networks. Furthermore, "Suicide" was directly connected to five symptoms of depression and four items of loneliness, with the strongest connections being between it and "Feeling of worthlessness" and "Psychomotor agitation/retardation".

CONCLUSIONS

Our findings provide a more nuanced explanation of the link between loneliness and depression and anxiety. The results identified the bridge symptom "People are around me but not with me", which had the strongest effect on enhancing symptoms of depression and anxiety. Clinical improvements based on the findings of this study and the impact of the intervention are discussed.